class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
      attndim (int): TCPGen attention dimension
      biasing (bool): If true, use biasing, otherwise use standard RNN-T
      deepbiasing (bool): If true, use deep biasing by extracting the biasing vector
      embdim (int): dimension of symbol embeddings
      jointdim (int): dimension of the joint network joint dimension
      charlist (list): The list of word piece tokens in the same order as the output layer
      encoutdim (int): dimension of the encoder output vectors
      dropout_tcpgen (float): dropout rate for TCPGen
      tcpsche (int): The epoch at which TCPGen starts to train
      DBaverage (bool): If true, instead of TCPGen, use DBRNNT for biasing
code: def forward_tcpgen(self, targets, ptrdist_mask, source_encodings):
      tcpgen_dist = None
      if self.DBaverage and self.deepbiasing:
          hptr = self.biasingemb(
              1 - ptrdist_mask[:, :, :-1].float()
          ).unsqueeze(1)
      else:
          query_char = self.predictor.embedding(targets)
          query_char = self.Qproj_char(query_char).unsqueeze(
              1
          )  # B * 1 * U * attndim
          query_acoustic = self.Qproj_acoustic(
              source_encodings
          ).unsqueeze(
              2
          )  # B * T * 1 * attndim
          query = query_char + query_acoustic  # B * T * U * attndim
          hptr, tcpgen_dist = self.get_tcpgen_distribution(
              query, ptrdist_mask
          )
      return hptr, tcpgen_dist
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 4 for calling the RNNTBiasing obj's underlying member method code with example usage: obj.forward_tcpgen(targets, ptrdist_mask, source_encodings) and returns: hptr,  tcpgen_dist